Overview

Dataset statistics

Number of variables32
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory367.6 KiB
Average record size in memory256.1 B

Variable types

Numeric17
Categorical15

Alerts

Age is highly overall correlated with TotalWorkingYearsHigh correlation
Department is highly overall correlated with JobRoleHigh correlation
JobLevel is highly overall correlated with MonthlyIncome and 1 other fieldsHigh correlation
JobRole is highly overall correlated with DepartmentHigh correlation
MaritalStatus is highly overall correlated with StockOptionLevelHigh correlation
MonthlyIncome is highly overall correlated with JobLevel and 1 other fieldsHigh correlation
PercentSalaryHike is highly overall correlated with PerformanceRatingHigh correlation
PerformanceRating is highly overall correlated with PercentSalaryHikeHigh correlation
StockOptionLevel is highly overall correlated with MaritalStatusHigh correlation
TotalWorkingYears is highly overall correlated with Age and 3 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
EmployeeNumber has unique valuesUnique
EducationField has 27 (1.8%) zerosZeros
JobRole has 131 (8.9%) zerosZeros
NumCompaniesWorked has 197 (13.4%) zerosZeros
TrainingTimesLastYear has 54 (3.7%) zerosZeros
YearsAtCompany has 44 (3.0%) zerosZeros
YearsInCurrentRole has 244 (16.6%) zerosZeros
YearsSinceLastPromotion has 581 (39.5%) zerosZeros
YearsWithCurrManager has 263 (17.9%) zerosZeros

Reproduction

Analysis started2024-06-02 15:53:04.060410
Analysis finished2024-06-02 15:54:33.725233
Duration1 minute and 29.66 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92381
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:33.904207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1353735
Coefficient of variation (CV)0.24741146
Kurtosis-0.40414514
Mean36.92381
Median Absolute Deviation (MAD)6
Skewness0.4132863
Sum54278
Variance83.455049
MonotonicityNot monotonic
2024-06-02T15:54:34.184675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35 78
 
5.3%
34 77
 
5.2%
36 69
 
4.7%
31 69
 
4.7%
29 68
 
4.6%
32 61
 
4.1%
30 60
 
4.1%
33 58
 
3.9%
38 58
 
3.9%
40 57
 
3.9%
Other values (33) 815
55.4%
ValueCountFrequency (%)
18 8
 
0.5%
19 9
 
0.6%
20 11
 
0.7%
21 13
 
0.9%
22 16
 
1.1%
23 14
 
1.0%
24 26
1.8%
25 26
1.8%
26 39
2.7%
27 48
3.3%
ValueCountFrequency (%)
60 5
 
0.3%
59 10
0.7%
58 14
1.0%
57 4
 
0.3%
56 14
1.0%
55 22
1.5%
54 18
1.2%
53 19
1.3%
52 18
1.2%
51 19
1.3%

Attrition
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1233 
1.0
237 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1233
83.9%
1.0 237
 
16.1%

Length

2024-06-02T15:54:34.448591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:34.692670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1233
83.9%
1.0 237
 
16.1%

Most occurring characters

ValueCountFrequency (%)
0 2703
61.3%
. 1470
33.3%
1 237
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2703
61.3%
. 1470
33.3%
1 237
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2703
61.3%
. 1470
33.3%
1 237
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2703
61.3%
. 1470
33.3%
1 237
 
5.4%

BusinessTravel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2.0
1043 
1.0
277 
0.0
150 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 1043
71.0%
1.0 277
 
18.8%
0.0 150
 
10.2%

Length

2024-06-02T15:54:34.915510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:35.174536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1043
71.0%
1.0 277
 
18.8%
0.0 150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 1620
36.7%
. 1470
33.3%
2 1043
23.7%
1 277
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1620
36.7%
. 1470
33.3%
2 1043
23.7%
1 277
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1620
36.7%
. 1470
33.3%
2 1043
23.7%
1 277
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1620
36.7%
. 1470
33.3%
2 1043
23.7%
1 277
 
6.3%

DailyRate
Real number (ℝ)

Distinct886
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.48571
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:35.450883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile165.35
Q1465
median802
Q31157
95-th percentile1424.1
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.5091
Coefficient of variation (CV)0.50282403
Kurtosis-1.2038228
Mean802.48571
Median Absolute Deviation (MAD)344
Skewness-0.0035185684
Sum1179654
Variance162819.59
MonotonicityNot monotonic
2024-06-02T15:54:36.340573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
691 6
 
0.4%
408 5
 
0.3%
530 5
 
0.3%
1329 5
 
0.3%
1082 5
 
0.3%
329 5
 
0.3%
829 4
 
0.3%
1469 4
 
0.3%
267 4
 
0.3%
217 4
 
0.3%
Other values (876) 1423
96.8%
ValueCountFrequency (%)
102 1
 
0.1%
103 1
 
0.1%
104 1
 
0.1%
105 1
 
0.1%
106 1
 
0.1%
107 1
 
0.1%
109 1
 
0.1%
111 3
0.2%
115 1
 
0.1%
116 2
0.1%
ValueCountFrequency (%)
1499 1
 
0.1%
1498 1
 
0.1%
1496 2
0.1%
1495 3
0.2%
1492 1
 
0.1%
1490 4
0.3%
1488 1
 
0.1%
1485 3
0.2%
1482 1
 
0.1%
1480 2
0.1%

Department
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1.0
961 
2.0
446 
0.0
 
63

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 961
65.4%
2.0 446
30.3%
0.0 63
 
4.3%

Length

2024-06-02T15:54:36.610996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:36.860137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 961
65.4%
2.0 446
30.3%
0.0 63
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 1533
34.8%
. 1470
33.3%
1 961
21.8%
2 446
 
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1533
34.8%
. 1470
33.3%
1 961
21.8%
2 446
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1533
34.8%
. 1470
33.3%
1 961
21.8%
2 446
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1533
34.8%
. 1470
33.3%
1 961
21.8%
2 446
 
10.1%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:37.087425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1068644
Coefficient of variation (CV)0.88189823
Kurtosis-0.2248334
Mean9.192517
Median Absolute Deviation (MAD)5
Skewness0.958118
Sum13513
Variance65.721251
MonotonicityNot monotonic
2024-06-02T15:54:37.337240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 211
14.4%
1 208
14.1%
10 86
 
5.9%
9 85
 
5.8%
3 84
 
5.7%
7 84
 
5.7%
8 80
 
5.4%
5 65
 
4.4%
4 64
 
4.4%
6 59
 
4.0%
Other values (19) 444
30.2%
ValueCountFrequency (%)
1 208
14.1%
2 211
14.4%
3 84
 
5.7%
4 64
 
4.4%
5 65
 
4.4%
6 59
 
4.0%
7 84
 
5.7%
8 80
 
5.4%
9 85
5.8%
10 86
5.9%
ValueCountFrequency (%)
29 27
1.8%
28 23
1.6%
27 12
0.8%
26 25
1.7%
25 25
1.7%
24 28
1.9%
23 27
1.8%
22 19
1.3%
21 18
1.2%
20 25
1.7%

Education
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
572 
4
398 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Length

2024-06-02T15:54:37.593009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:37.861785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

EducationField
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.247619
Minimum0
Maximum5
Zeros27
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:38.084626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3313691
Coefficient of variation (CV)0.59234642
Kurtosis-0.68808083
Mean2.247619
Median Absolute Deviation (MAD)1
Skewness0.55037125
Sum3304
Variance1.7725437
MonotonicityNot monotonic
2024-06-02T15:54:38.297032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 606
41.2%
3 464
31.6%
2 159
 
10.8%
5 132
 
9.0%
4 82
 
5.6%
0 27
 
1.8%
ValueCountFrequency (%)
0 27
 
1.8%
1 606
41.2%
2 159
 
10.8%
3 464
31.6%
4 82
 
5.6%
5 132
 
9.0%
ValueCountFrequency (%)
5 132
 
9.0%
4 82
 
5.6%
3 464
31.6%
2 159
 
10.8%
1 606
41.2%
0 27
 
1.8%

EmployeeNumber
Real number (ℝ)

UNIQUE 

Distinct1470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.8653
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:38.557045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.45
Q1491.25
median1020.5
Q31555.75
95-th percentile1967.55
Maximum2068
Range2067
Interquartile range (IQR)1064.5

Descriptive statistics

Standard deviation602.02433
Coefficient of variation (CV)0.58741801
Kurtosis-1.2231789
Mean1024.8653
Median Absolute Deviation (MAD)533.5
Skewness0.01657402
Sum1506552
Variance362433.3
MonotonicityStrictly increasing
2024-06-02T15:54:38.832442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1391 1
 
0.1%
1389 1
 
0.1%
1387 1
 
0.1%
1383 1
 
0.1%
1382 1
 
0.1%
1380 1
 
0.1%
1379 1
 
0.1%
1377 1
 
0.1%
1375 1
 
0.1%
Other values (1460) 1460
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
4 1
0.1%
5 1
0.1%
7 1
0.1%
8 1
0.1%
10 1
0.1%
11 1
0.1%
12 1
0.1%
13 1
0.1%
ValueCountFrequency (%)
2068 1
0.1%
2065 1
0.1%
2064 1
0.1%
2062 1
0.1%
2061 1
0.1%
2060 1
0.1%
2057 1
0.1%
2056 1
0.1%
2055 1
0.1%
2054 1
0.1%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
453 
4
446 
2
287 
1
284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Length

2024-06-02T15:54:39.110150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:39.372888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring characters

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1.0
882 
0.0
588 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 882
60.0%
0.0 588
40.0%

Length

2024-06-02T15:54:39.604563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:39.840213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 882
60.0%
0.0 588
40.0%

Most occurring characters

ValueCountFrequency (%)
0 2058
46.7%
. 1470
33.3%
1 882
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2058
46.7%
. 1470
33.3%
1 882
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2058
46.7%
. 1470
33.3%
1 882
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2058
46.7%
. 1470
33.3%
1 882
20.0%

HourlyRate
Real number (ℝ)

Distinct71
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.891156
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:40.091608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383.75
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation20.329428
Coefficient of variation (CV)0.30853044
Kurtosis-1.1963985
Mean65.891156
Median Absolute Deviation (MAD)18
Skewness-0.032310953
Sum96860
Variance413.28563
MonotonicityNot monotonic
2024-06-02T15:54:40.371600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 29
 
2.0%
98 28
 
1.9%
42 28
 
1.9%
48 28
 
1.9%
84 28
 
1.9%
57 27
 
1.8%
79 27
 
1.8%
96 27
 
1.8%
54 26
 
1.8%
52 26
 
1.8%
Other values (61) 1196
81.4%
ValueCountFrequency (%)
30 19
1.3%
31 15
1.0%
32 24
1.6%
33 19
1.3%
34 12
0.8%
35 18
1.2%
36 18
1.2%
37 18
1.2%
38 13
0.9%
39 17
1.2%
ValueCountFrequency (%)
100 19
1.3%
99 20
1.4%
98 28
1.9%
97 21
1.4%
96 27
1.8%
95 23
1.6%
94 22
1.5%
93 16
1.1%
92 25
1.7%
91 18
1.2%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Length

2024-06-02T15:54:40.656842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:40.909253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

JobLevel
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
543 
2
534 
3
218 
4
106 
5
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Length

2024-06-02T15:54:41.158612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:41.513223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

JobRole
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4585034
Minimum0
Maximum8
Zeros131
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:41.866264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4618213
Coefficient of variation (CV)0.55216315
Kurtosis-1.1927348
Mean4.4585034
Median Absolute Deviation (MAD)2
Skewness-0.35726992
Sum6554
Variance6.0605641
MonotonicityNot monotonic
2024-06-02T15:54:42.280080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 326
22.2%
6 292
19.9%
2 259
17.6%
4 145
9.9%
0 131
8.9%
3 102
 
6.9%
8 83
 
5.6%
5 80
 
5.4%
1 52
 
3.5%
ValueCountFrequency (%)
0 131
8.9%
1 52
 
3.5%
2 259
17.6%
3 102
 
6.9%
4 145
9.9%
5 80
 
5.4%
6 292
19.9%
7 326
22.2%
8 83
 
5.6%
ValueCountFrequency (%)
8 83
 
5.6%
7 326
22.2%
6 292
19.9%
5 80
 
5.4%
4 145
9.9%
3 102
 
6.9%
2 259
17.6%
1 52
 
3.5%
0 131
8.9%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Length

2024-06-02T15:54:42.737880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:43.188123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring characters

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

MaritalStatus
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1.0
673 
2.0
470 
0.0
327 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 673
45.8%
2.0 470
32.0%
0.0 327
22.2%

Length

2024-06-02T15:54:43.527932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:43.929337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 673
45.8%
2.0 470
32.0%
0.0 327
22.2%

Most occurring characters

ValueCountFrequency (%)
0 1797
40.7%
. 1470
33.3%
1 673
 
15.3%
2 470
 
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1797
40.7%
. 1470
33.3%
1 673
 
15.3%
2 470
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1797
40.7%
. 1470
33.3%
1 673
 
15.3%
2 470
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1797
40.7%
. 1470
33.3%
1 673
 
15.3%
2 470
 
10.7%

MonthlyIncome
Real number (ℝ)

HIGH CORRELATION 

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.9313
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:44.415152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.9568
Coefficient of variation (CV)0.72397455
Kurtosis1.0052327
Mean6502.9313
Median Absolute Deviation (MAD)2199
Skewness1.3698167
Sum9559309
Variance22164857
MonotonicityNot monotonic
2024-06-02T15:54:44.919434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2342 4
 
0.3%
6142 3
 
0.2%
2741 3
 
0.2%
2559 3
 
0.2%
2610 3
 
0.2%
2451 3
 
0.2%
5562 3
 
0.2%
3452 3
 
0.2%
2380 3
 
0.2%
6347 3
 
0.2%
Other values (1339) 1439
97.9%
ValueCountFrequency (%)
1009 1
0.1%
1051 1
0.1%
1052 1
0.1%
1081 1
0.1%
1091 1
0.1%
1102 1
0.1%
1118 1
0.1%
1129 1
0.1%
1200 1
0.1%
1223 1
0.1%
ValueCountFrequency (%)
19999 1
0.1%
19973 1
0.1%
19943 1
0.1%
19926 1
0.1%
19859 1
0.1%
19847 1
0.1%
19845 1
0.1%
19833 1
0.1%
19740 1
0.1%
19717 1
0.1%

MonthlyRate
Real number (ℝ)

Distinct1427
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.103
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:45.290860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3384.55
Q18047
median14235.5
Q320461.5
95-th percentile25431.9
Maximum26999
Range24905
Interquartile range (IQR)12414.5

Descriptive statistics

Standard deviation7117.786
Coefficient of variation (CV)0.4972916
Kurtosis-1.2149561
Mean14313.103
Median Absolute Deviation (MAD)6206.5
Skewness0.018577808
Sum21040262
Variance50662878
MonotonicityNot monotonic
2024-06-02T15:54:45.581817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4223 3
 
0.2%
9150 3
 
0.2%
9558 2
 
0.1%
12858 2
 
0.1%
22074 2
 
0.1%
25326 2
 
0.1%
9096 2
 
0.1%
13008 2
 
0.1%
12355 2
 
0.1%
7744 2
 
0.1%
Other values (1417) 1448
98.5%
ValueCountFrequency (%)
2094 1
0.1%
2097 1
0.1%
2104 1
0.1%
2112 1
0.1%
2122 1
0.1%
2125 2
0.1%
2137 1
0.1%
2227 1
0.1%
2243 1
0.1%
2253 1
0.1%
ValueCountFrequency (%)
26999 1
0.1%
26997 1
0.1%
26968 1
0.1%
26959 1
0.1%
26956 1
0.1%
26933 1
0.1%
26914 1
0.1%
26897 1
0.1%
26894 1
0.1%
26862 1
0.1%

NumCompaniesWorked
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6931973
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:45.822648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009
Coefficient of variation (CV)0.92752545
Kurtosis0.010213817
Mean2.6931973
Median Absolute Deviation (MAD)1
Skewness1.0264711
Sum3959
Variance6.240049
MonotonicityNot monotonic
2024-06-02T15:54:46.016845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 521
35.4%
0 197
 
13.4%
3 159
 
10.8%
2 146
 
9.9%
4 139
 
9.5%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
9 52
 
3.5%
8 49
 
3.3%
ValueCountFrequency (%)
0 197
 
13.4%
1 521
35.4%
2 146
 
9.9%
3 159
 
10.8%
4 139
 
9.5%
5 63
 
4.3%
6 70
 
4.8%
7 74
 
5.0%
8 49
 
3.3%
9 52
 
3.5%
ValueCountFrequency (%)
9 52
 
3.5%
8 49
 
3.3%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
4 139
 
9.5%
3 159
 
10.8%
2 146
 
9.9%
1 521
35.4%
0 197
 
13.4%

OverTime
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1054 
1.0
416 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1054
71.7%
1.0 416
 
28.3%

Length

2024-06-02T15:54:46.252640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:46.519198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1054
71.7%
1.0 416
 
28.3%

Most occurring characters

ValueCountFrequency (%)
0 2524
57.2%
. 1470
33.3%
1 416
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2524
57.2%
. 1470
33.3%
1 416
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2524
57.2%
. 1470
33.3%
1 416
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2524
57.2%
. 1470
33.3%
1 416
 
9.4%

PercentSalaryHike
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.209524
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:46.709569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6599377
Coefficient of variation (CV)0.2406346
Kurtosis-0.30059822
Mean15.209524
Median Absolute Deviation (MAD)2
Skewness0.82112798
Sum22358
Variance13.395144
MonotonicityNot monotonic
2024-06-02T15:54:46.911296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 210
14.3%
13 209
14.2%
14 201
13.7%
12 198
13.5%
15 101
6.9%
18 89
6.1%
17 82
 
5.6%
16 78
 
5.3%
19 76
 
5.2%
22 56
 
3.8%
Other values (5) 170
11.6%
ValueCountFrequency (%)
11 210
14.3%
12 198
13.5%
13 209
14.2%
14 201
13.7%
15 101
6.9%
16 78
 
5.3%
17 82
 
5.6%
18 89
6.1%
19 76
 
5.2%
20 55
 
3.7%
ValueCountFrequency (%)
25 18
 
1.2%
24 21
 
1.4%
23 28
 
1.9%
22 56
3.8%
21 48
3.3%
20 55
3.7%
19 76
5.2%
18 89
6.1%
17 82
5.6%
16 78
5.3%

PerformanceRating
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
1244 
4
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Length

2024-06-02T15:54:47.159817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:47.439388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring characters

ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
459 
4
432 
2
303 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Length

2024-06-02T15:54:47.645460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:47.903284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring characters

ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

StockOptionLevel
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
631 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Length

2024-06-02T15:54:48.148259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:48.429621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

TotalWorkingYears
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.279592
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:48.684296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7807817
Coefficient of variation (CV)0.68981057
Kurtosis0.91826954
Mean11.279592
Median Absolute Deviation (MAD)4
Skewness1.1171719
Sum16581
Variance60.540563
MonotonicityNot monotonic
2024-06-02T15:54:48.956739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 202
 
13.7%
6 125
 
8.5%
8 103
 
7.0%
9 96
 
6.5%
5 88
 
6.0%
7 81
 
5.5%
1 81
 
5.5%
4 63
 
4.3%
12 48
 
3.3%
3 42
 
2.9%
Other values (30) 541
36.8%
ValueCountFrequency (%)
0 11
 
0.7%
1 81
5.5%
2 31
 
2.1%
3 42
 
2.9%
4 63
4.3%
5 88
6.0%
6 125
8.5%
7 81
5.5%
8 103
7.0%
9 96
6.5%
ValueCountFrequency (%)
40 2
 
0.1%
38 1
 
0.1%
37 4
0.3%
36 6
0.4%
35 3
 
0.2%
34 5
0.3%
33 7
0.5%
32 9
0.6%
31 9
0.6%
30 7
0.5%

TrainingTimesLastYear
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:49.205947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2892706
Coefficient of variation (CV)0.46056569
Kurtosis0.49499299
Mean2.7993197
Median Absolute Deviation (MAD)1
Skewness0.55312417
Sum4115
Variance1.6622187
MonotonicityNot monotonic
2024-06-02T15:54:49.410034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
1 71
 
4.8%
6 65
 
4.4%
0 54
 
3.7%
ValueCountFrequency (%)
0 54
 
3.7%
1 71
 
4.8%
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
6 65
 
4.4%
ValueCountFrequency (%)
6 65
 
4.4%
5 119
 
8.1%
4 123
 
8.4%
3 491
33.4%
2 547
37.2%
1 71
 
4.8%
0 54
 
3.7%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
893 
2
344 
4
153 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Length

2024-06-02T15:54:49.665559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-02T15:54:49.916427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

YearsAtCompany
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0081633
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:50.169614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1265252
Coefficient of variation (CV)0.87419841
Kurtosis3.9355088
Mean7.0081633
Median Absolute Deviation (MAD)3
Skewness1.7645295
Sum10302
Variance37.53431
MonotonicityNot monotonic
2024-06-02T15:54:50.469248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 196
13.3%
1 171
11.6%
3 128
8.7%
2 127
8.6%
10 120
8.2%
4 110
 
7.5%
7 90
 
6.1%
9 82
 
5.6%
8 80
 
5.4%
6 76
 
5.2%
Other values (27) 290
19.7%
ValueCountFrequency (%)
0 44
 
3.0%
1 171
11.6%
2 127
8.6%
3 128
8.7%
4 110
7.5%
5 196
13.3%
6 76
 
5.2%
7 90
6.1%
8 80
5.4%
9 82
5.6%
ValueCountFrequency (%)
40 1
 
0.1%
37 1
 
0.1%
36 2
 
0.1%
34 1
 
0.1%
33 5
0.3%
32 3
0.2%
31 3
0.2%
30 1
 
0.1%
29 2
 
0.1%
27 2
 
0.1%

YearsInCurrentRole
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2292517
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:50.728255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137
Coefficient of variation (CV)0.85668513
Kurtosis0.47742077
Mean4.2292517
Median Absolute Deviation (MAD)3
Skewness0.91736316
Sum6217
Variance13.127122
MonotonicityNot monotonic
2024-06-02T15:54:50.953379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 372
25.3%
0 244
16.6%
7 222
15.1%
3 135
 
9.2%
4 104
 
7.1%
8 89
 
6.1%
9 67
 
4.6%
1 57
 
3.9%
6 37
 
2.5%
5 36
 
2.4%
Other values (9) 107
 
7.3%
ValueCountFrequency (%)
0 244
16.6%
1 57
 
3.9%
2 372
25.3%
3 135
 
9.2%
4 104
 
7.1%
5 36
 
2.4%
6 37
 
2.5%
7 222
15.1%
8 89
 
6.1%
9 67
 
4.6%
ValueCountFrequency (%)
18 2
 
0.1%
17 4
 
0.3%
16 7
 
0.5%
15 8
 
0.5%
14 11
 
0.7%
13 14
 
1.0%
12 10
 
0.7%
11 22
 
1.5%
10 29
2.0%
9 67
4.6%

YearsSinceLastPromotion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1877551
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:51.206913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2224303
Coefficient of variation (CV)1.4729392
Kurtosis3.6126731
Mean2.1877551
Median Absolute Deviation (MAD)1
Skewness1.98429
Sum3216
Variance10.384057
MonotonicityNot monotonic
2024-06-02T15:54:51.428914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
7 76
 
5.2%
4 61
 
4.1%
3 52
 
3.5%
5 45
 
3.1%
6 32
 
2.2%
11 24
 
1.6%
8 18
 
1.2%
Other values (6) 65
 
4.4%
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
3 52
 
3.5%
4 61
 
4.1%
5 45
 
3.1%
6 32
 
2.2%
7 76
 
5.2%
8 18
 
1.2%
9 17
 
1.2%
ValueCountFrequency (%)
15 13
 
0.9%
14 9
 
0.6%
13 10
 
0.7%
12 10
 
0.7%
11 24
 
1.6%
10 6
 
0.4%
9 17
 
1.2%
8 18
 
1.2%
7 76
5.2%
6 32
2.2%

YearsWithCurrManager
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1231293
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-06-02T15:54:51.678984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5681361
Coefficient of variation (CV)0.86539517
Kurtosis0.17105808
Mean4.1231293
Median Absolute Deviation (MAD)3
Skewness0.83345099
Sum6061
Variance12.731595
MonotonicityNot monotonic
2024-06-02T15:54:51.902144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 344
23.4%
0 263
17.9%
7 216
14.7%
3 142
9.7%
8 107
 
7.3%
4 98
 
6.7%
1 76
 
5.2%
9 64
 
4.4%
5 31
 
2.1%
6 29
 
2.0%
Other values (8) 100
 
6.8%
ValueCountFrequency (%)
0 263
17.9%
1 76
 
5.2%
2 344
23.4%
3 142
9.7%
4 98
 
6.7%
5 31
 
2.1%
6 29
 
2.0%
7 216
14.7%
8 107
 
7.3%
9 64
 
4.4%
ValueCountFrequency (%)
17 7
 
0.5%
16 2
 
0.1%
15 5
 
0.3%
14 5
 
0.3%
13 14
 
1.0%
12 18
 
1.2%
11 22
 
1.5%
10 27
 
1.8%
9 64
4.4%
8 107
7.3%

Interactions

2024-06-02T15:54:26.687386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:07.591221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:11.970775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:16.481783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:22.091163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:26.378652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:32.676072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:38.323797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:43.041303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:48.213960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:52.860719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:57.448815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:02.653676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:07.434875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:11.778213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:17.833264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:22.270250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:26.924457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:07.889081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:12.218981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:16.718671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:22.332691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:26.632920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:33.401789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:38.563271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:43.294179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:48.545814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:53.077902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:57.684306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:03.038870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:07.662406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:12.032795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:18.066498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:22.540605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:27.186396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:08.139835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:12.488011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:16.976975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:22.574573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:26.876879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:33.780770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:38.830313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-06-02T15:53:21.132724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:25.590393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:31.235063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:37.523652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:42.247256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:47.079012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:52.116419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:56.667200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:01.439797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:06.628932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:11.003412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:16.884991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:21.495765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:25.875299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:31.728654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:11.439887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:15.943688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:21.558385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:25.851028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:31.505931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:37.798071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:42.521380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:47.450325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:52.370735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:56.919139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:01.843082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:06.897946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:11.259214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:17.310933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:21.748223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:26.143606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:31.986980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:11.719136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:16.215060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:21.816863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:26.100422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:32.189024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:38.056120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:42.784968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:47.866238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:52.616127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:53:57.163602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:02.220295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:07.159516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:11.518653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:17.577554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:22.004964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-02T15:54:26.396664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-06-02T15:54:52.188474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
Age1.0000.2130.0410.0070.000-0.0190.153-0.045-0.0020.0060.0000.0290.0250.295-0.1280.0000.1410.4720.0170.3530.0000.0080.0000.0350.0930.6570.0000.0330.2520.1980.1740.195
Attrition0.2131.0000.123-0.0570.0770.0790.0000.018-0.0100.1150.009-0.0070.1320.2160.0780.0990.173-0.1980.0150.0310.243-0.0240.0000.0390.198-0.199-0.0520.095-0.190-0.181-0.053-0.175
BusinessTravel0.0410.1231.000-0.0000.000-0.0070.0000.030-0.0110.0000.0370.0280.0160.000-0.0050.0000.0350.028-0.0120.0340.024-0.0270.0000.0000.0000.0250.0150.000-0.022-0.025-0.035-0.024
DailyRate0.007-0.057-0.0001.0000.000-0.0030.0170.034-0.0520.0000.0310.0240.0160.000-0.0060.0000.0850.016-0.0320.0370.0000.0250.0000.0000.0400.021-0.0110.012-0.0100.007-0.038-0.005
Department0.0000.0770.0000.0001.0000.0300.0000.018-0.0040.0180.026-0.0070.0000.2120.7500.0290.0300.1650.022-0.0310.000-0.0060.0000.0200.000-0.0040.0430.0470.0340.0560.0200.024
DistanceFromHome-0.0190.079-0.007-0.0030.0301.0000.0000.0170.0390.0000.0300.0200.0280.0540.0160.0000.0000.0030.040-0.0100.0660.0300.0580.0250.015-0.003-0.0250.0000.0110.014-0.0050.004
Education0.1530.0000.0000.0170.0000.0001.000-0.0380.0430.0190.0000.0140.0000.0880.0050.0150.0000.120-0.0210.1350.0010.0040.0000.0160.0270.162-0.0240.0000.0640.0550.0320.051
EducationField-0.0450.0180.0300.0340.0180.017-0.0381.000-0.0050.0310.000-0.0260.0000.0910.0190.0170.000-0.035-0.028-0.0120.000-0.0020.0000.0400.032-0.0220.0490.027-0.001-0.0070.0130.008
EmployeeNumber-0.002-0.010-0.011-0.052-0.0040.0390.043-0.0051.0000.0000.0500.0350.0350.036-0.0040.0000.0000.0020.0120.0070.016-0.0080.0290.0550.068-0.0040.0270.0000.013-0.0010.008-0.005
EnvironmentSatisfaction0.0060.1150.0000.0000.0180.0000.0190.0310.0001.0000.000-0.0520.0340.000-0.0180.0000.019-0.0150.0370.0060.060-0.0300.0000.0000.000-0.014-0.0120.0000.0080.0200.026-0.002
Gender0.0000.0090.0370.0310.0260.0300.0000.0000.0500.0001.000-0.0000.0000.048-0.0400.0000.032-0.044-0.042-0.0410.0310.0100.0000.0000.000-0.049-0.0330.000-0.042-0.030-0.025-0.027
HourlyRate0.029-0.0070.0280.024-0.0070.0200.014-0.0260.035-0.052-0.0001.0000.0000.000-0.0200.0100.000-0.020-0.0150.0190.064-0.0100.0000.0000.052-0.0120.0000.000-0.029-0.034-0.052-0.014
JobInvolvement0.0250.1320.0160.0160.0000.0280.0000.0000.0350.0340.0000.0001.0000.0000.0030.0000.024-0.025-0.0180.0150.000-0.0170.0000.0000.0220.0060.0020.0000.0140.016-0.0080.037
JobLevel0.2950.2160.0000.0000.2120.0540.0880.0910.0360.0000.0480.0000.0001.000-0.0390.0000.0460.9200.0530.1780.000-0.0320.0000.0000.0690.735-0.0200.0000.4720.3910.2690.371
JobRole-0.1280.078-0.005-0.0060.7500.0160.0050.019-0.004-0.018-0.040-0.0200.003-0.0391.0000.0000.061-0.0440.006-0.0660.0000.0020.0000.0300.039-0.1480.0220.029-0.055-0.013-0.019-0.035
JobSatisfaction0.0000.0990.0000.0000.0290.0000.0150.0170.0000.0000.0000.0100.0000.0000.0001.0000.0000.005-0.003-0.0520.0220.0240.0260.0000.000-0.016-0.0120.0000.0120.0010.007-0.017
MaritalStatus0.1410.1730.0350.0850.0300.0000.0000.0000.0000.0190.0320.0000.0240.0460.0610.0001.000-0.0790.025-0.0530.0000.0140.0000.0250.581-0.0930.0110.000-0.071-0.065-0.018-0.047
MonthlyIncome0.472-0.1980.0280.0160.1650.0030.120-0.0350.002-0.015-0.044-0.020-0.0250.920-0.0440.005-0.0791.0000.0540.1900.000-0.0340.0000.0430.0560.710-0.0350.0000.4640.3950.2650.365
MonthlyRate0.0170.015-0.012-0.0320.0220.040-0.021-0.0280.0120.037-0.042-0.015-0.0180.0530.006-0.0030.0250.0541.0000.0200.000-0.0050.0150.0550.0000.013-0.0100.034-0.030-0.007-0.016-0.035
NumCompaniesWorked0.3530.0310.0340.037-0.031-0.0100.135-0.0120.0070.006-0.0410.0190.0150.178-0.066-0.052-0.0530.1900.0201.0000.0000.0000.0000.0000.0000.315-0.0470.051-0.171-0.128-0.067-0.144
OverTime0.0000.2430.0240.0000.0000.0660.0010.0000.0160.0600.0310.0640.0000.0000.0000.0220.0000.0000.0000.0001.000-0.0150.0000.0250.0000.000-0.0710.000-0.036-0.036-0.016-0.040
PercentSalaryHike0.008-0.024-0.0270.025-0.0060.0300.004-0.002-0.008-0.0300.010-0.010-0.017-0.0320.0020.0240.014-0.034-0.0050.000-0.0151.0000.9970.0270.000-0.026-0.0040.000-0.054-0.026-0.055-0.026
PerformanceRating0.0000.0000.0000.0000.0000.0580.0000.0000.0290.0000.0000.0000.0000.0000.0000.0260.0000.0000.0150.0000.0000.9971.0000.0000.0000.012-0.0170.0000.0170.033-0.0070.026
RelationshipSatisfaction0.0350.0390.0000.0000.0200.0250.0160.0400.0550.0000.0000.0000.0000.0000.0300.0000.0250.0430.0550.0000.0250.0270.0001.0000.0300.0040.0050.000-0.001-0.0210.0370.000
StockOptionLevel0.0930.1980.0000.0400.0000.0150.0270.0320.0680.0000.0000.0520.0220.0690.0390.0000.5810.0560.0000.0000.0000.0000.0000.0301.0000.0530.0030.0190.0650.0720.0280.054
TotalWorkingYears0.657-0.1990.0250.021-0.004-0.0030.162-0.022-0.004-0.014-0.049-0.0120.0060.735-0.148-0.016-0.0930.7100.0130.3150.000-0.0260.0120.0040.0531.000-0.0140.0000.5940.4930.3350.495
TrainingTimesLastYear0.000-0.0520.015-0.0110.043-0.025-0.0240.0490.027-0.012-0.0330.0000.002-0.0200.022-0.0120.011-0.035-0.010-0.047-0.071-0.004-0.0170.0050.003-0.0141.0000.0000.0010.0050.010-0.012
WorkLifeBalance0.0330.0950.0000.0120.0470.0000.0000.0270.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0340.0510.0000.0000.0000.0000.0190.0000.0001.0000.0050.0230.002-0.005
YearsAtCompany0.252-0.190-0.022-0.0100.0340.0110.064-0.0010.0130.008-0.042-0.0290.0140.472-0.0550.012-0.0710.464-0.030-0.171-0.036-0.0540.017-0.0010.0650.5940.0010.0051.0000.8540.5200.843
YearsInCurrentRole0.198-0.181-0.0250.0070.0560.0140.055-0.007-0.0010.020-0.030-0.0340.0160.391-0.0130.001-0.0650.395-0.007-0.128-0.036-0.0260.033-0.0210.0720.4930.0050.0230.8541.0000.5060.725
YearsSinceLastPromotion0.174-0.053-0.035-0.0380.020-0.0050.0320.0130.0080.026-0.025-0.052-0.0080.269-0.0190.007-0.0180.265-0.016-0.067-0.016-0.055-0.0070.0370.0280.3350.0100.0020.5200.5061.0000.467
YearsWithCurrManager0.195-0.175-0.024-0.0050.0240.0040.0510.008-0.005-0.002-0.027-0.0140.0370.371-0.035-0.017-0.0470.365-0.035-0.144-0.040-0.0260.0260.0000.0540.495-0.012-0.0050.8430.7250.4671.000

Missing values

2024-06-02T15:54:32.447451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-02T15:54:33.343491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
0411.02.011022.0121.0120.094327.042.059931947981.0113108016405
1490.01.02791.0811.0231.061226.021.051302490710.023441103310717
2371.02.013731.0224.0441.092212.032.02090239661.0153207330000
3330.01.013921.0341.0540.056316.031.029092315911.0113308338730
4270.02.05911.0213.0711.040312.021.034681663290.0123416332222
5320.01.010051.0221.0841.079312.042.030681186400.0133308227736
6590.02.013241.0333.01030.081412.011.02670996441.02041312321000
7300.02.013581.02411.01141.067312.030.026931333510.0224211231000
8380.01.02161.02331.01241.044234.032.09526878700.02142010239718
9360.02.012991.02733.01331.094320.031.052371657760.01332217327777
AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
1460290.02.04681.02843.0205440.073216.012.03785848910.0143205315404
1461501.02.04102.02832.0205541.039237.010.0108541658641.01332120333220
1462390.02.07222.02412.0205620.060247.041.012031882800.011311212220996
1463310.00.03251.0533.0205721.074324.012.09936378700.01932010239417
1464260.02.011672.0534.0206040.030218.032.029662137800.0183405234200
1465360.01.08841.02323.0206131.041422.041.025711229040.01733117335203
1466390.02.06131.0613.0206241.042230.011.099912145740.0153119537717
1467270.02.01551.0431.0206421.087424.021.06142517411.0204216036203
1468490.01.010232.0233.0206541.063227.021.053901324320.01434017329608
1469340.02.06281.0833.0206821.082422.031.044041022820.0123106344312